
We propose a framework that learns to detect drift by learning the presentation of each type of concept drift. As a result, in our framework, not only can concept drift be detected, the type of drift can also be identified to help us understand them.

We propose to transform the pre-training of a meta-detector into a few-shot learning problem, and thereby proposing a meta-learning method [1]} based on prototypical-networks [2]} to learn the meta-detector. In this meta-detector, various concept drift classes can be represented as a corresponding single prototype.

To improve the accuracy of detecting concept drift, we propose a stream-based active learning algorithm (SAL) which is capable of handling various concept drifts by adapting the meta-detector to the underlying distribution in the stream.

